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Thursday, September 15, 2016

This is a guest post by Deepan Patel at Milengo Ltd. This post is part of a series of upcoming posts that will provide varying LSP perspectives on MT
technology. I would invite other LSPs who may have an interest in sharing a view to come forward and contact me to discuss different potential
subjects. I may not always share the opinions of the guest writers, but I am a proponent of sharing differing views, and letting readers decide for
themselves what makes most sense to them. I do not edit these opinions except for minor typos and basic reading flow related edits. I may sometimes
highlight some statements in bold to highlight something I think is central to the view. I have added a presentation made by Deepan at TAUS, which is in
the public domain, to provide more detail on his user case context.

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One of the key factors in Milengo’s success in establishing a robust and sustainable framework for machine translation (MT) has been to make translators an
integral part of all our MT-related activities. Machine translation continues to be a divisive topic for translators for various reasons, and it is
paramount for organizations that offer MT-based localization solutions to engage in respectful dialogue with translators. It is a prerequisite to ensuring
successful implementation of any MT strategy. From my point of view, respectful dialogue entails addressing the very valid concerns that translators may
have, especially on topics such as post-editing, in a balanced manner.

There are many translators who for whatever reason do not want to post-edit machine-translated content at all and it is important to respect their reasons
for this. I have spoken to many translators who simply do not like the discipline of post-editing because they feel that it introduces a sort of negative
mindset into their work. Translation becomes less of a creative activity for them because the overt focus of post-editing is on error analysis and
correction.

A corollary to this is that translators can feel that post-editing requires them to lower their own expectations in terms of producing a highly polished
and stylish translation. Whilst post-editing they find themselves fighting the urge to completely rewrite sentences so that the style of language
corresponds to their own preferences, even in cases where only minor adjustments would be needed to produce a perfectly adequate sentence. To me these are
perfectly reasonable viewpoints and we never seek to coerce translators into performing post-editing work, if they do not want to.

A lot of translators have also expressed frustrations with the attitude of some of the language service providers (LSPs) with whom they work; having
post-editing work thrust at them without any discussion on whether the original content selected for post-editing is actually suitable for MT, not being
provided with clear enough directives on the key aspects to focus on during post-editing, and most importantly, no spirit of negotiation when it comes to
establishing fair remuneration for the task at hand.

I am surprised if LSPs do choose to engage with translators on post-editing assignments in the manner just described. In my opinion, such an approach can
only serve to thoroughly alienate translators and is ultimately detrimental to the objective of successful MT implementation.

When we started our own processes several years ago of introducing MT into our service spectrum, we were (and still are) heavily dependent on the guidance
of our translators in establishing the parameters under which MT could help to increase translator throughput. And perhaps even more importantly, our
translators help us to recognize the scenarios where MT does not really add much, if any, value to the translation process. As a result, our own approach
is much more focused on those scenarios where we are confident that MT makes sense, and we are consequently handling ever-increasing volumes of
localization work from our clients with post-editing workflows. For all of this we have our translators to thank – not only for helping to us shape a
consultative approach regarding MT with our clients, but of course without them we would never get the post-editing work completed!

The key has been to involve translators at every stage of a given testing scenario. Much of the work that we undertake during testing and evaluation phases
relates to the careful selection of bilingual content to be used during MT engine creation. Although we use and appreciate automated mechanisms for
extraction, consolidation and ‘cleansing’ of engine training data, it should never be forgotten that we are still dealing with language after all, and that
highly proficient linguists should also play a very valuable role in the data selection process.

For example, we ask our translators to help us design bilingual test, tuning, and terminology sets to be used for engine creation. These are constructed
based on analysis of the actual source content that is eventually intended for machine translation, and are really vital for us in being able to
effectively benchmark the performance of any engine that we train. Once an initial working engine is in place, our translators help us to verify the
automated evaluation metric scores generated during the training process, and to identify patterns of errors in the output which we seek to remedy as much
as possible in subsequent engine re-trainings. Eventually, the trial post-editing runs with our translators help us to agree on reasonable throughput
expectations and consequently a consensus on fair compensation for post-editing.

Ultimately we are strong advocates of highly collaborative working models with translators when it comes to testing and eventually implementing MT for a
given scenario. Having translators participate at every stage of a lengthy testing process means that they are in full possession of all relevant facts to
make informed decisions about whether a given MT engine adds value to their translation work or not. Similarly, we (Milengo) are able to shape our own
approach towards evaluating whether MT could work effectively or not for a given localization scenario based on the expert guidance of our translators. I
really cannot overstate the value of translators for us in all our MT-related activities.

About the writer

Deepan Patel is Milengo Ltd’s MT Specialist and has been working in the localization industry for seven years. He is a Modern Languages graduate from the
University of Oxford and a certified Memsource trainer.

1 comment:

I think another important aspect in respect of MT is the lack of interpretation. A good translation is always 1:1 but the best translation identifies even the sense behind the words. E.g. it's difficult for MT to translate metaphors... isn't it?